Abstract
Diabetic retinopathy (DR) is one of the common issues of diabetic mellitus that affects the eyesight of humans by causing lesions in their retinas. DR is mainly caused by the damage of blood vessels in the tissue of the retina, and it is one of the leading causes of visual impairment globally. It can even cause blindness if not detected in its early stages. To reduce the risk of eyesight loss, early detection and treatment are necessary. The manual process by ophthalmologists in the detection of DR requires much effort and time and is costly also. Many computer vision-based techniques reduce the manual effort for the automatic detection of DR. Machine learning is an important subset of computer vision mainly used in medical imaging for the detection of different diseases. This paper introduces a deep learning-based framework for the detection and classification of diabetic retinopathy, where we have trained a new customized convolutional neural network (CNN) model on two different benchmark datasets. This trained CNN model is tested on the separate test datasets. The detection performances of CNN are significantly encouraging, and it can assist to the doctors and radiologist in the early diagnosis of DR.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Taylor R, Batey D (eds) (2012) Handbook of retinal screening in diabetes: diagnosis and management. Wiley
Neuwirth J (1988) Diabetic retinopathy: what you should know
Agurto C, Murray V, Barriga E, Murillo S, Pattichis M, Davis H, …, Soliz P (2010) Multiscale AM-FM methods for diabetic retinopathy lesion detection. IEEE Trans Med Imaging 29(2):502–512
Gupta A, Chhikara R (2018) Diabetic retinopathy: present and past. Procedia Comput Sci 132:1432–1440
Alyoubi WL, Shalash WM, Abulkhair MF (2020) Diabetic retinopathy detection through deep learning techniques: a review. Inf Med Unlocked 100377
Esfahani MT, Ghaderi M, Kafiyeh R (2018) Classification of diabetic and normal fundus images using new deep learning method. Leonardo Electron J Pract Technol 17:233–248
Kaggle dataset [Online]. Available, https://www.kaggle.com/c/diabetic-retinopathy-detection/data
Jiang H, Yang K, Gao M, Zhang D, Ma H, Qian W (2019) An interpretable ensemble deep learning model for diabetic retinopathy disease classification. In: 2019 41st Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 2045–2048
Decencière E, Zhang X, Cazuguel G, Lay B, Cochener B, Trone C, …, Klein JC (2014) Feedback on a publicly distributed image database: the Messidor database. Image Anal Stereol 33(3):231–234
Liu YP, Li Z, Xu C, Li J, Liang R (2019). Referable diabetic retinopathy identification from eye fundus images with weighted path for convolutional neural network. Artif intell Med 99:101694
Hoover AD, Kouznetsova V, Goldbaum M (2000) Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imaging 19(3):203–210
Li X, Hu X, Yu L, Zhu L, Fu CW, Heng PA (2019) CANet: cross-disease attention network for joint diabetic retinopathy and diabetic macular edema grading. IEEE Trans Med Imaging 39(5):1483–1493
Porwal P, Pachade S, Kamble R, Kokare M, Deshmukh G, Sahasrabuddhe V, Meriaudeau F (2018) Indian diabetic retinopathy image dataset (IDRiD). IEEE Dataport. https://doi.org/10.21227/H25W98
Dutta S, Manideep BC, Basha SM, Caytiles RD, Iyengar NCSN (2018) Classification of diabetic retinopathy images by using deep learning models. Int J Grid Distrib Comput 11(1):89–106
Wang X, Lu Y, Wang Y, Chen WB (2018) Diabetic retinopathy stage classification using convolutional neural networks. In: 2018 IEEE international conference on information reuse and integration (IRI). IEEE, pp 465–471
Li T, Gao Y, Wang K, Guo S, Liu H, Kang H (2019) Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening. Inf Sci 501:511–522
APTOS dataset [Online]. Available, https://www.kaggle.com/c/aptos2019-blindness-detection/data
Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR workshop and conference proceedings, pp 249–256
Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning. PMLR, pp 448–456
Nair V, Hinton GE (2010) Rectified linear units improve restricted Boltzmann machines. In: Icml
Nagi J, Ducatelle F, Di Caro GA, Cireşan D, Meier U, Giusti A, …, Gambardella LM (2011) Max-pooling convolutional neural networks for vision-based hand gesture recognition. In: 2011 IEEE international conference on signal and image processing applications (ICSIPA). IEEE, pp 342–347
Bishop CM (2006) Pattern recognition. Mach Learn 128(9)
Gadekallu TR, Khare N, Bhattacharya S, Singh S, Reddy Maddikunta PK, Ra IH, Alazab M (2020) Early detection of diabetic retinopathy using PCA-firefly based deep learning model. Electronics 9(2):274
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ahmad, I., Singh, V.P., Agarwal, S. (2022). Detection of Diabetic Retinopathy Using Deep Learning-Based Framework. In: Agrawal, S., Gupta, K.K., Chan, J.H., Agrawal, J., Gupta, M. (eds) Machine Intelligence and Smart Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-9650-3_17
Download citation
DOI: https://doi.org/10.1007/978-981-16-9650-3_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9649-7
Online ISBN: 978-981-16-9650-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)